op {
  graph_op_name: "TensorScatterSub"
  in_arg {
    name: "tensor"
    description: <<END
Tensor to copy/update.
END
  }
  in_arg {
    name: "indices"
    description: <<END
Index tensor.
END
  }
  in_arg {
    name: "updates"
    description: <<END
Updates to scatter into output.
END
  }
  out_arg {
    name: "output"
    description: <<END
A new tensor copied from tensor and updates subtracted according to the indices.
END
  }
  summary: "Subtracts sparse `updates` from an existing tensor according to `indices`."
  description: <<END
This operation creates a new tensor by subtracting sparse `updates` from the
passed in `tensor`.
This operation is very similar to `tf.scatter_nd_sub`, except that the updates
are subtracted from an existing tensor (as opposed to a variable). If the memory
for the existing tensor cannot be re-used, a copy is made and updated.

`indices` is an integer tensor containing indices into a new tensor of shape
`shape`.  The last dimension of `indices` can be at most the rank of `shape`:

    indices.shape[-1] <= shape.rank

The last dimension of `indices` corresponds to indices into elements
(if `indices.shape[-1] = shape.rank`) or slices
(if `indices.shape[-1] < shape.rank`) along dimension `indices.shape[-1]` of
`shape`.  `updates` is a tensor with shape

    indices.shape[:-1] + shape[indices.shape[-1]:]

The simplest form of tensor_scatter_sub is to subtract individual elements
from a tensor by index. For example, say we want to insert 4 scattered elements
in a rank-1 tensor with 8 elements.

In Python, this scatter subtract operation would look like this:

```python
    indices = tf.constant([[4], [3], [1], [7]])
    updates = tf.constant([9, 10, 11, 12])
    tensor = tf.ones([8], dtype=tf.int32)
    updated = tf.tensor_scatter_nd_sub(tensor, indices, updates)
    print(updated)
```

The resulting tensor would look like this:

    [1, -10, 1, -9, -8, 1, 1, -11]

We can also, insert entire slices of a higher rank tensor all at once. For
example, if we wanted to insert two slices in the first dimension of a
rank-3 tensor with two matrices of new values.

In Python, this scatter add operation would look like this:

```python
    indices = tf.constant([[0], [2]])
    updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
                            [7, 7, 7, 7], [8, 8, 8, 8]],
                           [[5, 5, 5, 5], [6, 6, 6, 6],
                            [7, 7, 7, 7], [8, 8, 8, 8]]])
    tensor = tf.ones([4, 4, 4],dtype=tf.int32)
    updated = tf.tensor_scatter_nd_sub(tensor, indices, updates)
    print(updated)
```

The resulting tensor would look like this:

    [[[-4, -4, -4, -4], [-5, -5, -5, -5], [-6, -6, -6, -6], [-7, -7, -7, -7]],
     [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
     [[-4, -4, -4, -4], [-5, -5, -5, -5], [-6, -6, -6, -6], [-7, -7, -7, -7]],
     [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]]

Note that on CPU, if an out of bound index is found, an error is returned.
On GPU, if an out of bound index is found, the index is ignored.
END
}
